LlamaIndex started as an open-source RAG framework and has pivoted sharply into a commercial product: LlamaParse, an AI-powered document parsing and extraction platform built for enterprises. The core problem it solves is document ingestion — getting useful, structured data out of hard-to-read files like dense PDFs, scanned forms, tables, and multi-column reports so that downstream AI agents can actually use the content. The platform handles layout-aware parsing, schema-based field extraction (LlamaExtract), document segmentation (LlamaSplit), and spreadsheet parsing (LlamaSheets), all unified under the LlamaParse brand. Enterprises in finance, legal, healthcare, and insurance buy it to power RAG pipelines, autonomous agents, and automated document workflows — processing millions of pages a day. Developers adopt it through a self-serve API (Python/TypeScript SDKs), while enterprise deals layer on managed infrastructure, VPC deployment, and SLA-backed uptime. The open-source LlamaIndex framework, with ~50,000 GitHub stars, continues to serve as a top-of-funnel community engine feeding paid LlamaParse adoption.